Laser point cloud technology refers to a technology for sensing surrounding environment using a laser sensor installed in a mobile carrier (e.g., a vehicle) and processing the sensed information, thereby obtaining information about the environment where the mobile carrier is located, such as the lane it occupies, a road range, and a position of an obstacle.
In the prior art, road information is extracted mainly by constructing a road edge model based on a laser point cloud, constructing a road surface model corresponding to the laser point cloud by stochastically setting an initial input threshold of a regression algorithm, then obtaining a laser point cloud cluster corresponding to the laser point cloud, and obtaining an object corresponding to the laser point cloud cluster through point cloud segmentation and point cloud recognition.
In the scheme above, the road surface model corresponding to the laser point cloud is constructed by stochastically setting an initial input threshold. Constructing a road surface model in such a manner is not only inefficient, but also has relatively large errors; as a consequence, recognition of the object is rather inefficient with a relatively large error.